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FP7

EEEMBEDDED Report Summary

Project ID: 609349
Funded under: FP7-NMP
Country: Germany

Periodic Report Summary 2 - EEEMBEDDED (Collaborative Holistic Design Laboratory and Methodology for Energy-Efficient Embedded Buildings)

Project Context and Objectives:
eeEmbedded is an European FP7 industry-driven project established under Call EeB.NMP.2013-5 “Optimised design methodologies for energy-efficient buildings integrated in the neighbourhood energy systems”. It develops an open BIM-based holistic collaborative design and simulation platform, a related holistic design methodology, an energy system information model and an integrated information management framework for designing energy-efficient buildings and their optimal energetic embedding in the neighbourhood of surrounding buildings and energy systems. A new design control and monitoring system based on hierarchical verifiable design check points, so called Key Points (KP), will support the complex design collaboration process. Knowledge-based detailing templates will allow energy simulations as soon as in the early design phase and BIM-enabled interoperability will provide for a seamless design process with distributed experts, and a seamless integration of simulations in the virtual design office (energy performance, CO2, CFD, control system, energy system, climate change, user behaviour, construction, facility operation), thus extending it to a real virtual design lab. eeEmbedded focuses specifically on the following 7 RTD objectives:
(1) Interoperability of the design objectives as the baseline for collaborative holistic design using a new KP-based design methodology. The new developed Key Points will act as milestones of the design process and monitors the progress of the multi-disciplinary, multi-model and multi-physics design process.
(2) Interoperability of the information for heterogeneous distributed infor¬mation resources and services on the basis of system and domain ontology schemas and tools. An ontology-based open interoperability system as the baseline for managing the information and cross-dependencies of the multi information models, BIM, ESIM and BACS, and the related multi-physics problems and their computational analyses. Development of a new reference model schema for the Energy System Information Model (ESIM) structured according to BIM-IFC (ISO 16739) and incorporating the topography structure in accordance with cityGML.
(3) Interoperability of systems as the knowledge structure above the information domain models providing the mapping information needed to transform the domain information models to computational engineering models, as well as the knowledge needed to decide upon the necessary LoD and the respective cross-model structure.
(4) Holistic collaborative virtual design office, based on collaborative and virtual enterprise methods to manage people, tools and information, including a change management component to properly handle the various changes arising during design and enabling to find, retrieve and compare multiple different design alternatives with the help of an advanced BIM-based visualization system.
(5) Holistic virtual design lab providing quantitative computational support based on efficient cloud computing methods. The tools and services integrated in the virtual design lab will provide the computer power for the various required engineering analysis and simulation tasks and will support the feedback cycles for the interrelationships between the computational models, based on the provided interoperability of system information.
(6) Stochastic approach as part of the overall virtual design lab approach, extending current deterministic models and approaches in order to cope with the uncertainties in the lifecycle concerning climate, energy provision, as well as the usage of the building and the human behaviour.
(7) Development of a knowledge management system for fast and well-grounded design decision-making with characteristic design detailing templates enabling quantitative analyses and simulations as soon as in the early design phases.
The project runs from 01.10.2013 with duration of four years. The eeEmbedded consortium features 15 partners from 9 European countries.
Project Results:
In the first project period (01.10.2013-31.03.2015) the focus of the RTD work had been on the gap analysis, on the requirement setup for the Key Point driven holistic multi-disciplinary design method and on the development of the three eeE to-be Use Cases for urban, early and detailed phase (detailed in BPMN). We also set up eeBusiness models from the viewpoints of (1) owners, (2) ICT vendors and (3) designers as a basis for the upcoming tasks in the second and third period. Furthermore, all interoperability and collaboration requirements were specified to be used as a basis for the eeE continuous design process on tool and management level. To support design processes the advanced usage of knowledge-based templates were conceptually developed and requirements specified.
The overall architecture of the eeEmbedded platform, with the specifications of the major components and their principal interrelationships that culminates in the System Architecture for the virtual holistic design lab and office, was defined.
The specification of the overall holistic multi-disciplinary KP-based design framework and its software solutions within the eeEmbedded system architecture was done and finally all the periodic results were mapped into a holistic Key Point-controlled design method (see Figure 1), hierarchically structured dynamically evolving decision points and expressed in aggregated requirements. The conceptual discussion for setting up stochastic, risk and vulnerability models and control strategies had started as well as the process of defining, categorising and classification of the relevant domain models in the eeE context.
In the current second project period (01.04.2015-30.09.2016), the focus of the RTD work was especially on:
• Fully automated the urban and early design workflow using the eeE framework and scenarios
• eeE prognosis system has been developed and stochastic approaches have been integrated
• Energy and CO2 lifecycle performance analysis and cost assessment methods have been established
• The basic ICT methods, the distributed multi-information model and the multi-physics models have been developed
• The new ESIM Model, based on the BIM methodology, has been developed
• The concepts of the virtual design lab and office with a service-oriented SOA platform are realized prototypical
• Implementations of the virtual energy design lab and the virtual multidisciplinary collaborative design office, the Scenario Manager (ScM), are in progress with prototypical results
• Development of the Multimodel Navigation and Visualization systems and methods
• Preparation of the BIM data for the eeE Test case: a fictional building for a startup company, located in Weimar, Germany.
• Achieved first complete test runs through urban and early design phases

All results are explained roughly in the objectives (see Core of the report 2.1) and detailed in the WP section (see Core of the report 2.2). From a high level, the status and the progress could be described as:
The developed Service Oriented Architecture (SOA), as is shown in Figure 2, was extended and flashed out to cover the complex integration of multiple heterogeneous tools into one system as well as their collaboration. All components and services were further elaborated and a common ICT structure was established, which is built around a core of collaboration and management services, based on the ontology frameworks and bound together via REST interfaces: a) Local off-the-shelve applications such as CAD and FM systems, b) distributed cloud-enabled computational tools and c) a set of Multi Model and multi physics management web services. The basic structure of the eeE platform, the virtual energy design lab and a virtual multidisciplinary collaborative design office, was extended with the Key Point driven multidisciplinary design method. Summarized the main advancements lay in providing a complete collaborative and interoperable design framework, design process and working environment.
Potential Impact:
The major achieved results of the project by the end of the second period are:

KEY POINT DRIVEN MULTI-DISCIPLINARY DESIGN METHOD
Key Points guide the design process by providing building requirements and design criteria in form of target values, checked after all major design steps. They are categorized in Decision Values (DV), representing the preferences of decision makers related to the project goals, Key Performance Indicators (KPI), providing numeric metrics of the building energy performance, and Key Design Parameters (KDP) representing mandatory building properties in terms of allowed value ranges as explained in the results of the first period (Fig. 1).

DECISION LEVEL SUPPORT
The developed method supports decision-making in four clearly structured decision levels corresponding to respective process and data views. The Setup view addresses the translation of requirements to key points and their use to setup individual design tasks. The Designer View comprises the tasks related to the evolution and evaluation of the design based on the set KDPs. The Analyst View regards the energy and cost analysis and evaluation of proposed design alternatives based on the computed KPIs and the Decision Maker View regards the evaluation of the design alternatives based on the weighted Decision Values (Fig. 2).

INTEROPERABLE SERVICE ORIENTED ICT PLATFORM
The developed conceptual approach is technically enabled by a layered service-oriented platform. It comprises a User Layer including the actual value-add expert applications of the end users, a Virtual Lab Layer covering the functionalities on user side developed generically and configured specifically for each domain view, a Communication Layer providing a BCF-based Service Bus inter-connecting all the distributed component services and tools, a Service Layer providing services for all needed model, variant and simulation management functions, and a Repository Layer representing the common project-wide information storage (Fig. 3).

MULTI-MODEL FRAMEWORK
The developed platform tools and services are supported by a coherent multi-model framework based on standard BIM/IFC, a set of interoperable data access APIs and a novel Link Model approach enabling the flexible integration of distributed heterogeneous (non-BIM) information resources such as climate data, occupancy data etc. The relevant domain models encompass the Architectural Model and a new Energy System Information Model (ESIM), which provide the backbone of the framework, as well as BACS, HVAC, Energy Simulation, LCC, and LCA models. Data consistency is ensured by an overarching lean ontology, which provides for a uniform representation of the important multi-model inter-relationships also enabling data exchange requirements, KDP and KPI checks thereby ensuring high model and data quality.

VIRTUAL eeBIM LAB TOOLS
The Virtual Lab comprises two tools common for all actors - the developed Multi-Model Navigator (MMNav) and the Key Point Analysis (KPA) tool. MMNav enables visualizing the 3D BIM model, linking its components with external data, and can also show the results of the analysis/simulation tools and the performed model checks. The KPA tool provides a decision maker perspective by summarizing and configuring KPI results from all analysed variants into easy to understand and decide upon diagrams. Each Lab is also configured to include local or cloud-enabled design, analysis and simulation tools as appropriate for the respective actor profile as e.g. Allplan, DDS-CAD and the developed and/or extended analysis tools TRNSYS-TUD, 3D Wind and LCA/LCC as part of iTWO (Figs. 4-7).

COLLABORATION AND RESOURCE MANAGEMENT SERVICES
The developed Scenario Manager coordinates a structured project setup (based on IDM), supports each design step and manages data validation and data sharing. Additional services like Job Matrix, Model Combiner etc. are developed as resource management services and support the idea of intelligent BIM processes. (Fig. 8)
List of Websites:
www.eeEmbedded.eu

Related information

Reported by

TECHNISCHE UNIVERSITAET DRESDEN
Germany
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